Overview

Dataset statistics

Number of variables16
Number of observations19001
Missing cells7531
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory128.0 B

Variable types

Numeric10
Categorical5
DateTime1

Alerts

name has a high cardinality: 18780 distinct values High cardinality
host_name has a high cardinality: 6307 distinct values High cardinality
neighbourhood has a high cardinality: 215 distinct values High cardinality
id is highly correlated with host_idHigh correlation
host_id is highly correlated with idHigh correlation
number_of_reviews is highly correlated with reviews_per_monthHigh correlation
reviews_per_month is highly correlated with number_of_reviewsHigh correlation
id is highly correlated with host_idHigh correlation
host_id is highly correlated with idHigh correlation
number_of_reviews is highly correlated with reviews_per_monthHigh correlation
reviews_per_month is highly correlated with number_of_reviewsHigh correlation
number_of_reviews is highly correlated with reviews_per_monthHigh correlation
reviews_per_month is highly correlated with number_of_reviewsHigh correlation
id is highly correlated with host_idHigh correlation
host_id is highly correlated with idHigh correlation
neighbourhood_group is highly correlated with latitude and 1 other fieldsHigh correlation
latitude is highly correlated with neighbourhood_group and 1 other fieldsHigh correlation
longitude is highly correlated with neighbourhood_group and 1 other fieldsHigh correlation
room_type is highly correlated with priceHigh correlation
price is highly correlated with room_typeHigh correlation
number_of_reviews is highly correlated with reviews_per_monthHigh correlation
reviews_per_month is highly correlated with number_of_reviewsHigh correlation
last_review has 3758 (19.8%) missing values Missing
reviews_per_month has 3758 (19.8%) missing values Missing
minimum_nights is highly skewed (γ1 = 26.36588078) Skewed
name is uniformly distributed Uniform
id has unique values Unique
number_of_reviews has 3758 (19.8%) zeros Zeros
availability_365 has 6970 (36.7%) zeros Zeros

Reproduction

Analysis started2022-02-21 16:32:51.910814
Analysis finished2022-02-21 16:33:13.159022
Duration21.25 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct19001
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18830405.22
Minimum2539
Maximum36485609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.6 KiB
2022-02-21T17:33:13.289471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2539
5-th percentile1177971
Q19355498
median19387536
Q328919516
95-th percentile35240782
Maximum36485609
Range36483070
Interquartile range (IQR)19564018

Descriptive statistics

Standard deviation10969857.88
Coefficient of variation (CV)0.5825609034
Kurtosis-1.225663309
Mean18830405.22
Median Absolute Deviation (MAD)9804926
Skewness-0.06687371468
Sum3.577965297 × 1011
Variance1.203377819 × 1014
MonotonicityNot monotonic
2022-02-21T17:33:13.471791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91386641
 
< 0.1%
179221811
 
< 0.1%
137469621
 
< 0.1%
209177121
 
< 0.1%
267342881
 
< 0.1%
105275461
 
< 0.1%
95181
 
< 0.1%
121886511
 
< 0.1%
289596081
 
< 0.1%
151581941
 
< 0.1%
Other values (18991)18991
99.9%
ValueCountFrequency (%)
25391
< 0.1%
38311
< 0.1%
50221
< 0.1%
51211
< 0.1%
52031
< 0.1%
52381
< 0.1%
58031
< 0.1%
60901
< 0.1%
68481
< 0.1%
77501
< 0.1%
ValueCountFrequency (%)
364856091
< 0.1%
364850571
< 0.1%
364802921
< 0.1%
364797231
< 0.1%
364783431
< 0.1%
364721711
< 0.1%
364718961
< 0.1%
364688801
< 0.1%
364586681
< 0.1%
364568291
< 0.1%

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct18780
Distinct (%)98.9%
Missing7
Missing (%)< 0.1%
Memory size148.6 KiB
Hillside Hotel
 
7
Brooklyn Apartment
 
7
Home away from home
 
6
Private Room
 
6
New york Multi-unit building
 
5
Other values (18775)
18963 

Length

Max length179
Median length36
Mean length36.75139518
Min length1

Characters and Unicode

Total characters72
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18619 ?
Unique (%)98.0%

Sample

1st rowPrivate Lg Room 15 min to Manhattan
2nd rowTIME SQUARE CHARMING ONE BED IN HELL'S KITCHEN,NYC
3rd rowVoted #1 Location Quintessential 1BR W Village Apt
4th rowSpacious 1 bedroom apartment 15min from Manhattan
5th rowBig beautiful bedroom in huge Bushwick apartment

Common Values

ValueCountFrequency (%)
Hillside Hotel7
 
< 0.1%
Brooklyn Apartment7
 
< 0.1%
Home away from home6
 
< 0.1%
Private Room6
 
< 0.1%
New york Multi-unit building5
 
< 0.1%
Cozy Room5
 
< 0.1%
Private room in Manhattan5
 
< 0.1%
Private room in Williamsburg4
 
< 0.1%
Cozy Private Room4
 
< 0.1%
Williamsburg Loft3
 
< 0.1%
Other values (18770)18942
99.7%
(Missing)7
 
< 0.1%

Length

2022-02-21T17:33:13.681651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in6594
 
5.7%
room4014
 
3.5%
3161
 
2.7%
bedroom3016
 
2.6%
private2874
 
2.5%
apartment2658
 
2.3%
cozy2017
 
1.7%
apt1765
 
1.5%
brooklyn1623
 
1.4%
studio1556
 
1.3%
Other values (6673)86285
74.7%

Most occurring characters

ValueCountFrequency (%)
72
100.0%

Most occurring categories

ValueCountFrequency (%)
Control72
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common72
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
72
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII72
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
72
100.0%

host_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16241
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66394588.99
Minimum2571
Maximum274273284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.6 KiB
2022-02-21T17:33:13.867415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2571
5-th percentile764688
Q17728754
median30487854
Q3104835356
95-th percentile239519185
Maximum274273284
Range274270713
Interquartile range (IQR)97106602

Descriptive statistics

Standard deviation77826632.15
Coefficient of variation (CV)1.172183356
Kurtosis0.2898665825
Mean66394588.99
Median Absolute Deviation (MAD)27185317
Skewness1.245958822
Sum1.261563585 × 1012
Variance6.056984672 × 1015
MonotonicityNot monotonic
2022-02-21T17:33:14.034297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219517861117
 
0.6%
10743442371
 
0.4%
3028359444
 
0.2%
13735886636
 
0.2%
1224305136
 
0.2%
6139196334
 
0.2%
1609895833
 
0.2%
2254157332
 
0.2%
2637726324
 
0.1%
285674822
 
0.1%
Other values (16231)18552
97.6%
ValueCountFrequency (%)
25711
 
< 0.1%
27873
< 0.1%
31511
 
< 0.1%
34151
 
< 0.1%
35631
 
< 0.1%
36472
< 0.1%
43961
 
< 0.1%
48691
 
< 0.1%
50891
 
< 0.1%
60411
 
< 0.1%
ValueCountFrequency (%)
2742732841
< 0.1%
2741954581
< 0.1%
2741033831
< 0.1%
2738701231
< 0.1%
2738416671
< 0.1%
2737415771
< 0.1%
2736568901
< 0.1%
2736322921
< 0.1%
2736193041
< 0.1%
2736131061
< 0.1%

host_name
Categorical

HIGH CARDINALITY

Distinct6307
Distinct (%)33.2%
Missing8
Missing (%)< 0.1%
Memory size148.6 KiB
Michael
 
159
David
 
157
John
 
130
Sonder (NYC)
 
117
Alex
 
98
Other values (6302)
18332 

Length

Max length35
Median length6
Mean length6.100931922
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4077 ?
Unique (%)21.5%

Sample

1st rowIris
2nd rowJohlex
3rd rowJohn
4th rowRegan
5th rowMegan

Common Values

ValueCountFrequency (%)
Michael159
 
0.8%
David157
 
0.8%
John130
 
0.7%
Sonder (NYC)117
 
0.6%
Alex98
 
0.5%
Daniel92
 
0.5%
Sarah87
 
0.5%
Maria86
 
0.5%
Chris81
 
0.4%
Anna77
 
0.4%
Other values (6297)17909
94.3%

Length

2022-02-21T17:33:14.220932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
401
 
1.9%
and234
 
1.1%
michael175
 
0.8%
david169
 
0.8%
sonder153
 
0.7%
john145
 
0.7%
nyc122
 
0.6%
alex121
 
0.6%
laura117
 
0.6%
maria105
 
0.5%
Other values (5877)19369
91.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

neighbourhood_group
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size148.6 KiB
Brooklyn
8046 
Manhattan
8031 
Queens
2331 
Bronx
 
434
Staten Island
 
159

Length

Max length13
Median length8
Mean length8.150623651
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQueens
2nd rowManhattan
3rd rowManhattan
4th rowQueens
5th rowBrooklyn

Common Values

ValueCountFrequency (%)
Brooklyn8046
42.3%
Manhattan8031
42.3%
Queens2331
 
12.3%
Bronx434
 
2.3%
Staten Island159
 
0.8%

Length

2022-02-21T17:33:14.381711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-21T17:33:14.484941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn8046
42.0%
manhattan8031
41.9%
queens2331
 
12.2%
bronx434
 
2.3%
staten159
 
0.8%
island159
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

neighbourhood
Categorical

HIGH CARDINALITY

Distinct215
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size148.6 KiB
Williamsburg
1526 
Bedford-Stuyvesant
1478 
Harlem
 
1086
Bushwick
 
978
Upper West Side
 
734
Other values (210)
13199 

Length

Max length26
Median length12
Mean length11.91947792
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.1%

Sample

1st rowSunnyside
2nd rowHell's Kitchen
3rd rowWest Village
4th rowAstoria
5th rowBushwick

Common Values

ValueCountFrequency (%)
Williamsburg1526
 
8.0%
Bedford-Stuyvesant1478
 
7.8%
Harlem1086
 
5.7%
Bushwick978
 
5.1%
Upper West Side734
 
3.9%
East Village705
 
3.7%
Hell's Kitchen693
 
3.6%
Upper East Side667
 
3.5%
Crown Heights631
 
3.3%
Midtown505
 
2.7%
Other values (205)9998
52.6%

Length

2022-02-21T17:33:14.601629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
east2575
 
8.4%
side1756
 
5.7%
harlem1540
 
5.0%
williamsburg1526
 
5.0%
bedford-stuyvesant1478
 
4.8%
heights1427
 
4.7%
upper1401
 
4.6%
village1186
 
3.9%
west1033
 
3.4%
bushwick978
 
3.2%
Other values (227)15760
51.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

latitude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12087
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.72806312
Minimum40.50873
Maximum40.91306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.6 KiB
2022-02-21T17:33:14.996717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40.50873
5-th percentile40.64456
Q140.68882
median40.72171
Q340.76321
95-th percentile40.82645
Maximum40.91306
Range0.40433
Interquartile range (IQR)0.07439

Descriptive statistics

Standard deviation0.05538931047
Coefficient of variation (CV)0.001359978998
Kurtosis0.05864487992
Mean40.72806312
Median Absolute Deviation (MAD)0.03659
Skewness0.2542518047
Sum773873.9274
Variance0.003067975714
MonotonicityNot monotonic
2022-02-21T17:33:15.178392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.718138
 
< 0.1%
40.722328
 
< 0.1%
40.686348
 
< 0.1%
40.694148
 
< 0.1%
40.686837
 
< 0.1%
40.726077
 
< 0.1%
40.680847
 
< 0.1%
40.724346
 
< 0.1%
40.763896
 
< 0.1%
40.707416
 
< 0.1%
Other values (12077)18930
99.6%
ValueCountFrequency (%)
40.508731
< 0.1%
40.522931
< 0.1%
40.538711
< 0.1%
40.539391
< 0.1%
40.541061
< 0.1%
40.543121
< 0.1%
40.54551
< 0.1%
40.548571
< 0.1%
40.548891
< 0.1%
40.549011
< 0.1%
ValueCountFrequency (%)
40.913061
< 0.1%
40.905271
< 0.1%
40.903911
< 0.1%
40.903561
< 0.1%
40.903291
< 0.1%
40.902811
< 0.1%
40.90261
< 0.1%
40.899811
< 0.1%
40.898111
< 0.1%
40.897561
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION

Distinct9944
Distinct (%)52.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.95082679
Minimum-74.23914
Maximum-73.71795
Zeros0
Zeros (%)0.0%
Negative19001
Negative (%)100.0%
Memory size148.6 KiB
2022-02-21T17:33:15.368865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-74.23914
5-th percentile-74.00369
Q1-73.98205
median-73.95463
Q3-73.93449
95-th percentile-73.86186
Maximum-73.71795
Range0.52119
Interquartile range (IQR)0.04756

Descriptive statistics

Standard deviation0.04682498719
Coefficient of variation (CV)-0.0006331908543
Kurtosis4.824279693
Mean-73.95082679
Median Absolute Deviation (MAD)0.02489
Skewness1.234020827
Sum-1405139.66
Variance0.002192579425
MonotonicityNot monotonic
2022-02-21T17:33:15.551322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.948299
 
< 0.1%
-73.957259
 
< 0.1%
-73.951219
 
< 0.1%
-73.957429
 
< 0.1%
-73.954279
 
< 0.1%
-73.985899
 
< 0.1%
-73.956758
 
< 0.1%
-73.95678
 
< 0.1%
-73.951498
 
< 0.1%
-73.980438
 
< 0.1%
Other values (9934)18915
99.5%
ValueCountFrequency (%)
-74.239141
< 0.1%
-74.212381
< 0.1%
-74.196261
< 0.1%
-74.182591
< 0.1%
-74.176281
< 0.1%
-74.173881
< 0.1%
-74.171171
< 0.1%
-74.170651
< 0.1%
-74.169661
< 0.1%
-74.166341
< 0.1%
ValueCountFrequency (%)
-73.717951
< 0.1%
-73.718291
< 0.1%
-73.725821
< 0.1%
-73.727161
< 0.1%
-73.727311
< 0.1%
-73.72741
< 0.1%
-73.727781
< 0.1%
-73.728171
< 0.1%
-73.729011
< 0.1%
-73.729281
< 0.1%

room_type
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size148.6 KiB
Entire home/apt
9522 
Private room
9041 
Shared room
 
438

Length

Max length15
Median length15
Mean length13.48034314
Min length11

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowPrivate room

Common Values

ValueCountFrequency (%)
Entire home/apt9522
50.1%
Private room9041
47.6%
Shared room438
 
2.3%

Length

2022-02-21T17:33:15.713112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-21T17:33:15.811427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
entire9522
25.1%
home/apt9522
25.1%
room9479
24.9%
private9041
23.8%
shared438
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct321
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.3404558
Minimum10
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.6 KiB
2022-02-21T17:33:15.930081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile40
Q166
median100
Q3160
95-th percentile270
Maximum350
Range340
Interquartile range (IQR)94

Descriptive statistics

Standard deviation71.53034564
Coefficient of variation (CV)0.5846826807
Kurtosis0.5028006941
Mean122.3404558
Median Absolute Deviation (MAD)45
Skewness1.027024411
Sum2324591
Variance5116.590347
MonotonicityNot monotonic
2022-02-21T17:33:16.104813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100856
 
4.5%
150821
 
4.3%
50636
 
3.3%
200590
 
3.1%
75570
 
3.0%
60555
 
2.9%
80531
 
2.8%
70482
 
2.5%
120471
 
2.5%
65471
 
2.5%
Other values (311)13018
68.5%
ValueCountFrequency (%)
106
< 0.1%
112
 
< 0.1%
121
 
< 0.1%
131
 
< 0.1%
151
 
< 0.1%
163
 
< 0.1%
181
 
< 0.1%
193
 
< 0.1%
2013
0.1%
213
 
< 0.1%
ValueCountFrequency (%)
350147
0.8%
34914
 
0.1%
3481
 
< 0.1%
3472
 
< 0.1%
3461
 
< 0.1%
34511
 
0.1%
3441
 
< 0.1%
3432
 
< 0.1%
3421
 
< 0.1%
3412
 
< 0.1%

minimum_nights
Real number (ℝ≥0)

SKEWED

Distinct75
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.906899637
Minimum1
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.6 KiB
2022-02-21T17:33:16.292805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile30
Maximum1250
Range1249
Interquartile range (IQR)4

Descriptive statistics

Standard deviation21.45654397
Coefficient of variation (CV)3.10653768
Kurtosis1156.455229
Mean6.906899637
Median Absolute Deviation (MAD)1
Skewness26.36588078
Sum131238
Variance460.3832792
MonotonicityNot monotonic
2022-02-21T17:33:16.474503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15003
26.3%
24619
24.3%
33086
16.2%
301442
 
7.6%
41253
 
6.6%
51140
 
6.0%
7822
 
4.3%
6285
 
1.5%
14210
 
1.1%
10178
 
0.9%
Other values (65)963
 
5.1%
ValueCountFrequency (%)
15003
26.3%
24619
24.3%
33086
16.2%
41253
 
6.6%
51140
 
6.0%
6285
 
1.5%
7822
 
4.3%
852
 
0.3%
934
 
0.2%
10178
 
0.9%
ValueCountFrequency (%)
12501
 
< 0.1%
9992
 
< 0.1%
4801
 
< 0.1%
4001
 
< 0.1%
3701
 
< 0.1%
36511
0.1%
3641
 
< 0.1%
3001
 
< 0.1%
2991
 
< 0.1%
2402
 
< 0.1%

number_of_reviews
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct321
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.79774749
Minimum0
Maximum607
Zeros3758
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size148.6 KiB
2022-02-21T17:33:16.662493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q324
95-th percentile116
Maximum607
Range607
Interquartile range (IQR)23

Descriptive statistics

Standard deviation45.49345456
Coefficient of variation (CV)1.911670614
Kurtosis19.56592146
Mean23.79774749
Median Absolute Deviation (MAD)6
Skewness3.706018813
Sum452181
Variance2069.654408
MonotonicityNot monotonic
2022-02-21T17:33:16.828996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03758
19.8%
12022
 
10.6%
21342
 
7.1%
3975
 
5.1%
4796
 
4.2%
5589
 
3.1%
6544
 
2.9%
7495
 
2.6%
8449
 
2.4%
9383
 
2.0%
Other values (311)7648
40.3%
ValueCountFrequency (%)
03758
19.8%
12022
10.6%
21342
 
7.1%
3975
 
5.1%
4796
 
4.2%
5589
 
3.1%
6544
 
2.9%
7495
 
2.6%
8449
 
2.4%
9383
 
2.0%
ValueCountFrequency (%)
6071
< 0.1%
5941
< 0.1%
5101
< 0.1%
4881
< 0.1%
4741
< 0.1%
4671
< 0.1%
4661
< 0.1%
4591
< 0.1%
4481
< 0.1%
4411
< 0.1%

last_review
Date

MISSING

Distinct1494
Distinct (%)9.8%
Missing3758
Missing (%)19.8%
Memory size148.6 KiB
Minimum2011-05-12 00:00:00
Maximum2019-07-08 00:00:00
2022-02-21T17:33:17.010354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:17.200289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct789
Distinct (%)5.2%
Missing3758
Missing (%)19.8%
Infinite0
Infinite (%)0.0%
Mean1.380927639
Minimum0.01
Maximum27.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.6 KiB
2022-02-21T17:33:17.371256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.19
median0.72
Q32.01
95-th percentile4.69
Maximum27.95
Range27.94
Interquartile range (IQR)1.82

Descriptive statistics

Standard deviation1.689988413
Coefficient of variation (CV)1.22380664
Kurtosis11.9516004
Mean1.380927639
Median Absolute Deviation (MAD)0.62
Skewness2.435330583
Sum21049.48
Variance2.856060837
MonotonicityNot monotonic
2022-02-21T17:33:17.531374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02362
 
1.9%
1352
 
1.9%
0.05333
 
1.8%
0.03323
 
1.7%
0.04268
 
1.4%
0.08256
 
1.3%
0.16244
 
1.3%
0.09235
 
1.2%
0.06226
 
1.2%
0.11217
 
1.1%
Other values (779)12427
65.4%
(Missing)3758
 
19.8%
ValueCountFrequency (%)
0.0117
 
0.1%
0.02362
1.9%
0.03323
1.7%
0.04268
1.4%
0.05333
1.8%
0.06226
1.2%
0.07168
0.9%
0.08256
1.3%
0.09235
1.2%
0.1191
1.0%
ValueCountFrequency (%)
27.951
< 0.1%
20.941
< 0.1%
19.751
< 0.1%
17.821
< 0.1%
16.221
< 0.1%
13.451
< 0.1%
13.421
< 0.1%
13.241
< 0.1%
13.151
< 0.1%
12.991
< 0.1%

calculated_host_listings_count
Real number (ℝ≥0)

Distinct47
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.583811378
Minimum1
Maximum327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.6 KiB
2022-02-21T17:33:17.709470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile13
Maximum327
Range326
Interquartile range (IQR)1

Descriptive statistics

Standard deviation31.15475023
Coefficient of variation (CV)4.732023511
Kurtosis77.21481439
Mean6.583811378
Median Absolute Deviation (MAD)0
Skewness8.461222905
Sum125099
Variance970.6184621
MonotonicityNot monotonic
2022-02-21T17:33:17.875832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
112627
66.5%
22605
 
13.7%
31123
 
5.9%
4539
 
2.8%
5345
 
1.8%
6208
 
1.1%
7165
 
0.9%
8164
 
0.9%
327117
 
0.6%
998
 
0.5%
Other values (37)1010
 
5.3%
ValueCountFrequency (%)
112627
66.5%
22605
 
13.7%
31123
 
5.9%
4539
 
2.8%
5345
 
1.8%
6208
 
1.1%
7165
 
0.9%
8164
 
0.9%
998
 
0.5%
1069
 
0.4%
ValueCountFrequency (%)
327117
0.6%
23271
0.4%
12144
 
0.2%
10336
 
0.2%
9669
0.4%
9134
 
0.2%
8732
 
0.2%
6519
 
0.1%
5241
 
0.2%
5016
 
0.1%

availability_365
Real number (ℝ≥0)

ZEROS

Distinct366
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.7253829
Minimum0
Maximum365
Zeros6970
Zeros (%)36.7%
Negative0
Negative (%)0.0%
Memory size148.6 KiB
2022-02-21T17:33:18.052810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median39
Q3219
95-th percentile358
Maximum365
Range365
Interquartile range (IQR)219

Descriptive statistics

Standard deviation130.5998995
Coefficient of variation (CV)1.190243279
Kurtosis-0.9348291358
Mean109.7253829
Median Absolute Deviation (MAD)39
Skewness0.8027018468
Sum2084892
Variance17056.33374
MonotonicityNot monotonic
2022-02-21T17:33:18.227560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06970
36.7%
365432
 
2.3%
364195
 
1.0%
1176
 
0.9%
5133
 
0.7%
3115
 
0.6%
2113
 
0.6%
179112
 
0.6%
89111
 
0.6%
6110
 
0.6%
Other values (356)10534
55.4%
ValueCountFrequency (%)
06970
36.7%
1176
 
0.9%
2113
 
0.6%
3115
 
0.6%
4107
 
0.6%
5133
 
0.7%
6110
 
0.6%
796
 
0.5%
885
 
0.4%
983
 
0.4%
ValueCountFrequency (%)
365432
2.3%
364195
1.0%
36388
 
0.5%
36262
 
0.3%
36144
 
0.2%
36033
 
0.2%
35949
 
0.3%
35853
 
0.3%
35733
 
0.2%
35626
 
0.1%

Interactions

2022-02-21T17:33:10.537136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:55.291524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:57.335459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:58.919812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:00.600383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:02.218865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:04.073470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:05.613622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:07.204047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:08.697896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:10.699343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:55.480297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:57.495370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:59.084044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:00.761444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:02.387063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:04.228795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:05.775561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:07.362182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:08.864851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:10.855795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:55.629675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:57.647216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:59.255281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:00.912008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:02.790430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:04.377456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:05.928643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:07.506870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:09.252494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:11.019694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:56.222530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:57.813429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:59.423453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:01.073667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:02.958603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:04.538080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:06.092208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:07.662803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:09.427271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:11.170834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:56.380244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:57.967751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:59.582000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:01.235970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:03.118482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:04.691741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:06.250660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:07.803699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:09.586744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:11.331118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:56.537399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:58.132838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:59.750233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:01.423873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:03.279155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:04.847375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:06.406139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:07.950547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:09.747223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:11.482661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:56.703612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:58.297667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:59.920682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:01.584147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:03.438282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:05.000915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:06.562926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:08.094460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:09.902370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:11.637231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:56.867017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:58.454639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:00.089487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:01.740398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:03.597776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:05.151370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:06.719578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:08.245079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:10.058571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:11.789279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:57.014422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:58.604886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:00.256655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:01.894811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:03.750755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:05.300225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:06.876294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:08.392616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:10.218240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:11.942826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:57.173938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:32:58.764368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:00.432261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:02.055190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:03.912899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:05.453814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:07.041273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:08.546582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-21T17:33:10.377036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-21T17:33:18.401552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-21T17:33:18.620545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-21T17:33:18.837020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-21T17:33:19.033699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-21T17:33:19.414473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-21T17:33:12.204904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-21T17:33:12.589774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-21T17:33:12.856275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-21T17:33:13.005131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
09138664Private Lg Room 15 min to Manhattan47594947IrisQueensSunnyside40.74271-73.92493Private room74262019-05-260.1315
131444015TIME SQUARE CHARMING ONE BED IN HELL'S KITCHEN,NYC8523790JohlexManhattanHell's Kitchen40.76682-73.98878Entire home/apt17030NaTNaN1188
28741020Voted #1 Location Quintessential 1BR W Village Apt45854238JohnManhattanWest Village40.73631-74.00611Entire home/apt2453512018-09-191.1210
334602077Spacious 1 bedroom apartment 15min from Manhattan261055465ReganQueensAstoria40.76424-73.92351Entire home/apt125312019-05-240.65113
423203149Big beautiful bedroom in huge Bushwick apartment143460MeganBrooklynBushwick40.69839-73.92044Private room65282019-06-230.5228
54402805LRG 2br BKLYN APT CLOSE TO TRAINS AND PARK22807362JennyBrooklynProspect-Lefferts Gardens40.66025-73.96270Entire home/apt120332018-08-280.05116
630070126✩Prime Renovated 1/1 Apartment in Upper East Side✩4968673SeanManhattanUpper East Side40.76831-73.95929Entire home/apt200522019-05-260.68171
734231172Fully renovated brick house floor in Brooklyn59642348KevinBrooklynSunset Park40.64550-74.01262Entire home/apt95192019-07-089.001106
85856760Renovated 1BR in exciting, convenient area29408349ChadManhattanChinatown40.71490-73.99976Entire home/apt179572017-04-180.1410
97929441Beautiful Loft w/ Waterfront View!1453898AnthonyBrooklynWilliamsburg40.71268-73.96676Private room10522322019-06-195.00364

Last rows

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
189915192459Quiet Room in 4BR UWS Brownstone10677483GregManhattanUpper West Side40.80173-73.96625Private room7010NaTNaN10
189921327940Huge Gorgeous Park View Apartment!3290436HadarBrooklynFlatbush40.65335-73.96257Entire home/apt1203132016-08-270.282327
1899323612681Shared Room 1 Stop from Manhattan on the F Train55724558TaylorQueensLong Island City40.76006-73.94080Private room55422019-06-010.65589
1899434485745Midtown Manhattan Stunner - Private room261632622RoyaltonManhattanTheater District40.75491-73.98507Private room100132019-06-163.009318
1899525616250Stylish, spacious, private 1BR apt in Ditmas Park125396920AdamBrooklynFlatbush40.64314-73.95705Entire home/apt753102019-01-030.8410
189967094539Tranquil haven in bubbly Brooklyn2052211AdrianaBrooklynWindsor Terrace40.65360-73.97546Entire home/apt1431422016-08-270.04110
189974424261Large 1 BR with backyard on UWS3447311SarahManhattanUpper West Side40.80188-73.96808Entire home/apt2002222019-05-210.5010
189984545882Amazing studio/Loft with a backyard23569951KavehManhattanUpper East Side40.78110-73.94567Entire home/apt2203282019-05-230.501293
1899926518547U2 comfortable double bed sleeps 2 guests295128Carol GloriaBronxClason Point40.81225-73.85502Private room80142019-07-011.487365
1900033631782Private Bedroom in Williamsburg Apt!8569221AndiBrooklynWilliamsburg40.71829-73.95819Private room109332019-04-281.07297